On Logical Analysis of Ordinary Sentences

The prime purpose of this paper is, first, to restore to discourse-bound occasion sentences their rightful central place in semantics and secondly, taking these as the basic propositional elements in the logical analysis of language, to contribute to the development of an adequate logic of occasion sentences and a mathematical (Boolean) foundation for such a logic, thus preparing the ground for more adequate semantic, logical and mathematical foundations of the study of natural language. Some of the insights elaborated in this paper have appeared in the literature over the past thirty years, and a number of new developments have resulted from them. The present paper aims atproviding an integrated conceptual basis for this new development in semantics. In Section 1 it is argued that the reduction by translation of occasion sentences to eternal sentences, as proposed by Russell and Quine, is semantically and thus logically inadequate. Natural language is a system of occasion sentences, eternal sentences being merely boundary cases. The logic hasfewer tasks than is standardly assumed, as it excludes semantic calculi, which depend crucially on information supplied by cognition and context and thus belong to cognitive psychology rather than to logic. For sentences to express a proposition and thus be interpretable and informative, they must first be properly anchored in context. A proposition has a truth value when it is, moreover, properly keyed in the world, i.e. is about a situation in the world. Section 2 deals with the logical properties of natural language. It argues that presuppositional phenomena require trivalence and presents the trivalent logic PPC3, with two kinds of falsity and two negations. It introduces the notion of Σ-space for a sentence A (or A/A, the set of situations in which A is true) as the basis of logical model theory, and the notion of P A / (the Σ-space of the presuppositions of A), functioning as a `private' subuniverse for A/A. The trivalent Kleene calculus is reinterpreted as a logical account of vagueness, rather than of presupposition. PPC3 and the Kleene calculus are refinements of standard bivalent logic and can be combined into one logical system. In Section 3 the adequacy of PPC3 as a truth-functional model of presupposition is considered more closely and given a Boolean foundation. In a noncompositional extended Boolean algebra, three operators are defined: 1a for the conjoined presuppositions of a, a for the complement of a within 1a, and â for the complement of 1a within Boolean 1. The logical properties of this extended Boolean algebra are axiomatically defined and proved for all possible models. Proofs are provided of the consistency and the completeness of the system. Section 4 is a provisional exploration of the possibility of using the results obtained for a new discourse-dependent account of the logic of modalities in natural language. The overall result is a modified and refined logical and model-theoretic machinery, which takes into account both the discourse-dependency of natural language sentences and the necessity of selecting a key in the world before a truth value can be assigned.

Extracting perceived landscape properties from text sources

Even though Linked Data is a fairly young concept, querying its stores renews the old challenge of querying data in an easy and yet sufficiently expressive way. But, if in case of querying traditional relational databases, the knowledge of complete structure was feasible, in case of Linked Data it is far more difficult to have an exhaustive view. From the perspective of facilitating the access to the data to end-users with no prior expertise, the most natural solution would be querying in natural language. But, from the technological point of view, Natural Language Processing still has many limitations. In this context, we introduce our first results towards a system for building SPARQL queries from NL sentences that is based on sentences' grammatical structure, general structural patterns and ontological descriptions. We focus on processing queries from pharmacology data of Bio2RDF project.

From Stories—via Arguments, Scenarios, and Cases—to Probabilities: Commentary on Floris J. Bex's “The Hybrid Theory of Stories and Arguments Applied to the Simonshaven Case” and Bart Verheij's “Analyzing the Simonshaven Case With and Without Probabilities”

Montague grammarians claim that the logical analysis of language sketched in standard textbooks of first order logic is too coarse to be appropriate to such natural languages as English. In the following we shall defend the opposite view - at least as far as a very narrow fragment of English is concerned, traditional logical analysis is perfectly adequate provided we are ready to pay the price of abandoning classical logic in favour of logics containing a sufficiently rich stock of logical constants. Within the framework of Casari's comparative logic, we shall outline a model of a restricted fragment of English. We shall address such issues as the construction of complex noun phrases, the distinction between gradable and vague adjectives, the structure of comparative sentences and of antonym adjectival pairs

Senses are the extensions of language and interrelations between word meanings within language and the basis of flexible language use. Computers are basically made to process symbols. Computational linguistics is to simulate part of, if not all of, the natural language competence. From the perspective of computational linguistics, this paper sheds some light on semantic formalization, and proposes tagging of senses along with tagging weight values when processing word meanings. Also, this paper makes attempts to apply sense weight values to sentence processing in computational linguistics. Keywords-semantics, sense, weight, formalization, computational linguistics I. SEMANTIC KNOWLEDGE IN LANGUAGE PROCESSING According to prototype theory, semantic knowledge is part of human’s cognitive and belief systems. Semantics is also the critical research area since its initiation in 1893 by Michael Breal, and then by Trier and Lakoff who contributes to semantic research from both the descriptive and generative perspective. In linguistic semantics, rules and principles explain how literal meaning is coded in language and how to understand the “senses” of an expression or sense-relations that hold between one word and other expressions within the same language (Ceusters et al, 1999). Language is the carrier of both thoughts and forms. Thoughts are to induce, deduce, infer and judge, while senses are the contents and basis of thoughts. Senses are not the repeated addition of forms, rather a psychological process. Semantics is the mapping of cognitive structure from language expressions. Based on concepts, which are the abstract and general categorization of objects, people can apply semantic principles, situational contexts and encyclopedic knowledge to construct cognitive schema of the outside world. With frequent interactions and cognitive strategies, limited forms can be used to express unlimited connotation of thoughts (Tesser and Leone, 1977). For example, same meanings can be expressed with different semantic forms. a) 他这个人真有意思 (funny); b) 于是人们以为他们有了意思 (wish); c) 你向他意思意思 (express)如何; d) .我根本没有那个意思 (thought); e) 他常说人生真没有意思 (nonsense); f) 你们这么说是什么意思 (intention) These sentences are easy to comprehend. People not only use the grammatical structures but also the sense weights to process the words. The sense weight schema are stored in memory, and activated to represent and distill other sense weights. In this chain activation, sense weights are restructured and created to reflect prototypes. For example, “你好?”, “你好么?”, “好么你?”. Different structures are used in the above examples, but their essential meaning remains the same. So, semantic knowledge is of utmost importance in language comprehension. II. THE STATUS QUO OF COMPUTATIONAL LINGUISTICS Three domains and three corresponding levels of study are involved in building natural language processing systems, i.e. theoretical linguistics and grammar level, computational linguistics and formalization level, computer technology and realization level. Among them, grammar level concerns the innate characteristics of language and paves the way for the latter modules. Realization level focuses on how to control computer operation and provides the efficient development tools and environment. Formalization level is the interface of grammar level and realization level, with the task to readjust the general grammar modules to form grammar modules for convenient computer processing. Computational linguists’ mission is to establish formal grammars (Kaufmann and Pfister, 2012). Computational linguistics is also to establish formalized mathematical modules to analyze and process natural language, and develop computer programs to make it possible that machines can simulate part of, if not all of, human behavior and competence. Computers can only be used to process symbols and all those processed by computers are symbolized with no exception of languages (Yushi, 2003). Meanwhile, there have been breakthroughs in processing Chinese characters and phrases, so sentence level processing has become critical. Basic researches in syntactic, semantic and pragmatic knowledge are the frontier studies in Chinese processing. And how to acquire syntactic and semantic knowledge is the key, while the latter constitutes the major difficulty in processing the word level senses. As aforementioned, semantic formalization can take on many impossible missions in artificial intelligence and computational linguistics, and face challenges at the same time. Thus, the researcher will use semantic field to investigate formalization issues. Semantic fields are the sememe systems, which include the semantemes to represent similarities and differences, and are classified by the subordinates, i.e. degree and number of abstraction of Proceedings of the 2nd International Conference on Computer Science and Electronics Engineering (ICCSEE 2013) Published by Atlantis Press, Paris, France.

Background. Modern society is developing in a world of information overload and digitalization, which has led to a need for artificial intelligence (AI) to help analyze and process information more efficiently. Most AI models use natural language processing, and while they can imitate human speech, they generate texts based on templates set by humans. This creates an online language space that is unique to AI, with its own characteristics due to its isolation from human interaction. Purpose – analysis of lexical and grammatical features of artificial intelligence language and comparison with human language. Materials and methods. To solve the tasks set in the research, we used various methods of analysis: observation, description, and the comparative method. The article is based on an analysis of texts generated by neural networks such as ChatGPT, Rytr, Smodin, and ChatSonic, which were created at the request of the authors. Results. The functioning of artificial intelligence (AI) is based on simulating the thought processes that are inherent in the human mind. This imitation is made possible by the development of language modules by AI specialists for use by neural networks in natural language processing (NLP). The language space of AI, limited by the input data from these modules, can generate texts that are practically indistinguishable from natural language, although they have features that are determined by the conditions under which they are created. These features include attachment to keywords in the task, potential loss of the main idea, inconsistency with required style, repetition of similar ideas, lack of specific facts or personal experiences, and limitations in vocabulary, linguistic patterns, and grammatical structures. The current inability of artificial intelligence (AI) to go beyond its current linguistic capabilities is due to a lack of a concept such as consciousness in neural networks. This allows for the accumulation of experience and the ability to think with insights, which are essential for AI to progress beyond its current limitations.

Natural language processing (NLP) is a branch of artificial intelligence, and is a computational technique which analyzes and synthesizes the natural language written and spoken by humans. The human brain can recognize natural language instantly, but for a machine to understand it, this raw data needs to be processed. Computer applications which work for varying structures of languages can be built using NLP. This process will take unstructured data (human language) as input, and after processing, it will be converted to computer-readable languages. This complex process must follow various attributes and the grammatical structure of natural language. NLP performs syntactic and semantic analysis to identify data patterns hidden in natural language. Syntactic analysis, also called parsing, is a phase whose purpose is to draw the exact meaning of an NLP statement. Formal grammar rules of the underlying language are considered while processing the input. The main aim of parsing is to ensure that the string symbols in natural language comply with the grammar rules. The Graph of Words model is an approach that can be used as a better alternative to the Bag of Words model for text processing. A graph representation of the input documents focuses on word independence by considering the order of words and the distance between relevant words.

Natural Language Processing focuses on the interaction between computers and the natural language used by humans. In other words, the computer process extracts information from input in the form of natural language and / or produces output which is also in the form of natural language.Newspapers are printed sheets containing reports that occur in the community. However, the titles of newspapers often have an ambiguous meaning. When a written or spoken word, phrase, sentence, or even paragraph contains ambiguity, it is automatically ineffective and communicative for the reader or listener. For this reason, it is necessary to identify sentences to make it easier for readers to understand the meaning of an ambiguous title.To make it easier to identify ambiguous sentences in newspapers requires a shift Reduce parsing algorithm, using the Shift Reduce parsing algorithm can be useful to find out what the news writer wants to convey.

Introduction A pressing question related to the well-being of deaf children is how they develop a strong language base (e.g. Liben 1978). First or native language proficiency plays a vital role in many aspects of their development, ranging from social development to educational attainment to their learning of a second language. The target linguistic system should be easy to learn and use. A natural signed language is clearly a good choice for deaf children. While spoken English is a natural language, it is less obvious that a signed form of English is also a natural language. At issue is the development of Manually Coded English (MCE), which can be described as a form of language planning aimed at making English visible for deaf children (Ramsey 1989). MCE demonstrates a living experiment in which deaf children are expected to learn signed English as well as hearing children do spoken English. If MCE is a natural language, learning it should be effortless, with learning patterns consistent with what we know about natural language acquisition in general. American Sign Language (ASL) is a good example of how a sign system is defined as a natural language with the capacity of becoming a native language for deaf children, especially those of deaf parents who use ASL at home (Newport and Meier 1985; Meier 1991). However appropriate ASL is for deaf children of deaf parents, it is not the case that all deaf children are exposed to ASL.

Consider a huge, untagged speech corpus from a language without a written tradition. How can we quickly and accurately measure vowel space, without expending large amounts of labour and funds? We present a methodology that can be used to measure probabilistic variation across large corpora of natural spoken languages, particularly useful for under-resourced and lesser-documented languages. Using a heuristic function, the optimal vowel sample for any given phone category can be found. This heuristic is trained through machine learning, in this case, an unsupervised neural network. This process allows us to test large amounts of raw data, and create a vowel space, without the need to hand-tag many hours of recordings. We aim to model how speakers from different dialect groups speak—what are the phonetic patterns they are most likely to show, and can we differentiate and categorize unknown samples using these models created from natural language? This work uses spontaneous speech data in the endangered language Faetar, from the Heritage Language Variation and Change Corpus.

At present it is most important that information systems not only provide more powerful functionality and performance under the technologies of various data processing facilities and high‐quality data communication means, but also develop more effectual user interface with a view to constructing software development environments. The concept of integration is to support cooperative interfaces among various kinds of processing facilities, and is one of many methods which can provide effectual user interface systematically. In this paper, the integration mechanism in an information system is discussed from the viewpoint of the development of an advanced user interface. First, the logical structures and the physical structures are distinguished from the data structures, which are manipulated by individual processing facilities. Then each data object is controlled uniformly on the basis of these logical structures. The logical structure is specified definitely by users and the physical structure is managed practically by processing facilities. The approach in this paper specifies the logical structures of individual data objects hierarchically with each other, matches these logical structures uniformly, and finally, controls a unified user interface under the cooperative mechanism of processing facilities.

In the early 1930s, Carnap's philosophical programme underwent a radical change of method, which mainly consisted of two successive breakthroughs. These have been carefully distinguished by Steve Awodey and André Carus, who have also fully articulated the historical and philosophical context in which they occurred.1 The first step was taken in January 1931 during a sleepless night when 'the whole theory of language structure and its possible applications in philosophy came to [Carnap] like a vision' (Carnap 1963a, p. 53), a new theory which ingeniously combined an axiomatic (Hilbert-style) approach to language and a purely formal syntactical method of analysis, worked out from a standpoint that Carnap then called 'metalogic'. At that time, it was far from clear that the full development of this method required a different essentially richer metalanguage, a point that Carnap would nevertheless have to acknowledge before long. This first step was then followed by a second one, taken in all likelihood in October 1932, when he adopted the principle of tolerance, an idea which he later explained in more detail. In The Logical Syntax of Language (1934), this principle is stated in the often quoted following terms: 'In logic there are no morals. Everyone is at liberty to build up his own logic, i.e. his own form of language, as he wishes' (Carnap 1934d/1937, §17). As a result of these two breakthroughs, from then on and through the rest of his philosophical career, Carnap kept two main ideas as key elements of his method. The first one is the explicit distinction between a language and a metalanguage, with the adoption of a metalinguistic viewpoint for what he called the 'logic of science', with which he proposed to replace traditional philosophy.KeywordsLanguage SystemCompleteness TheoremLinguistic CommunityLogical PluralismLinguistic BehaviourThese keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

An authoring tool for decision support systems in context questions of ecological knowledge

This article is a sequel to an article titled “A New Direction in AI – Toward a Computational Theory of Perceptions”, which appeared in the Spring 2001 issue of AI Magazine (volume 22, No. 1, 73-84). The concept of precisiated natural language (PNL) was briefly introduced in that article, and PNL was employed as a basis for computation with perceptions. In what follows, the conceptual structure of PNL is described in greater detail, and PNL’s role in knowledge representation, deduction, and concept definition is outlined and illustrated by examples. What should be understood is that PNL is in its initial stages of development and that the exposition that follows is an outline of the basic ideas that underlie PNL rather than a definitive theory. A natural language is basically a system for describing perceptions. Perceptions, such as perceptions of distance, likelihood, relevance, and most other attributes of physical and mental objects are intrinsically imprecise, reflecting the bounded ability of sensory organs, and ultimately the brain, to resolve detail and store information. In this perspective, the imprecision of natural languages is a direct consequence of the imprecision of perceptions [44, 45]. How can a natural language be precisiated – precisiated in the sense of making it possible to treat propositions drawn from a natural language as objects of computation? This is what PNL attempts to do. In PNL, precisiation is accomplished through translation into what is termed a precisiation language. In the case of PNL, the precisiation language is the generalized-constraint language (GCL), a language whose elements are so-called generalized constraints and their combinations. What distinguishes GCL from languages such as Prolog, LISP, SQL, and, more generally, languages associated with various logical systems, for example, predicate logic, modal logic, and so on, is its so much higher expressive power. The conceptual structure of PNL mirrors two fundamental facets of human cognition, (a) partiality and (b) granularity [43]. Partiality relates to the fact that most human concepts are not bivalent, that is, are a matter of degree. Thus, we have partial understanding, partial truth, partial possibility, partial certainty, partial similarity, and partial relevance, to cite a few examples. Similarly, granularity and granulation relate to clumping of values of attributes, forming granules with words as labels, for example, young, middle-aged, and old as labels of granules of age.